The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic stress...

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The Psychiatric Genomics Consortium Posttraumatic StressDisorder Workgroup: Posttraumatic stress disorder enters theage of large-scale genomic collaboration

Mark W Logue, Ananda B Amstadter, Dewleen G Baker,Laramie Duncan, Karestan C Koenen, Israel Liberzon, MarkW Miller, Rajendra A Morey, Caroline M Nievergelt, Kerry JRessler, Alicia K Smith, Jordan W Smoller, Murray B Stein,Jennifer A Sumner, Monica Uddin

Cite this article as: Mark W Logue, Ananda B Amstadter, Dewleen G Baker,Laramie Duncan, Karestan C Koenen, Israel Liberzon, Mark W Miller, RajendraA Morey, Caroline M Nievergelt, Kerry J Ressler, Alicia K Smith, Jordan WSmoller, Murray B Stein, Jennifer A Sumner, Monica Uddin, The PsychiatricGenomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumaticstress disorder enters the age of large-scale genomic collaboration, Neuropsycho-pharmacology accepted article preview 23 April 2015; doi: 10.1038/npp.2015.118.

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Received 26 November 2014; revised 10 March 2015; accepted 25 March 2015;Accepted article preview online 23 April 2015

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Logue et al. 1

The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic stress disorder enters the age of large-scale genomic collaboration. Mark W. Logue Ph.D.1-3*, Ananda B. Amstadter, Ph.D.4, Dewleen G. Baker M.D.5-6, Laramie Duncan Ph.D.7-9, Karestan C. Koenen Ph.D.10, Israel Liberzon M.D.11-12, Mark W. Miller Ph.D.13-14, Rajendra A. Morey M.D.15-17, Caroline M. Nievergelt Ph.D. 5-6, Kerry J. Ressler M.D. Ph.D18-20, Alicia K. Smith Ph.D.18, Jordan W. Smoller M.D., Sc.D.21-

23, Murray B. Stein M.D. M.P.H.5, Jennifer A. Sumner, Ph.D.10, Monica Uddin Ph.D.24-25. 1) Research, VA Boston Healthcare System, Boston, MA. 2) Biomedical Genetics, Boston University School of Medicine, Boston, MA. 3) Biostatistics, Boston University School of Public Health, Boston, MA. 4) Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond VA. 5) Department of Psychiatry, University of California at San Diego, La Jolla, CA. 6) VA San Diego Healthcare System, VA Center of Excellence for Stress and Mental Health (CESAMH), La Jolla, CA. 7) Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA. 8) Analytic and Translational Genetics Unit, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA. 9) Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA. 10) Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 11) Department of Psychiatry, University of Michigan, Ann Arbor, MI. 12) Veterans Affairs Ann Arbor Health System, Ann Arbor, MI. 13) National Center for PTSD, VA Boston Healthcare System, Boston, MA. 14) Department of Psychiatry, Boston University School of Medicine, Boston, MA. 15) Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC. 16) Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC. 17) Mental Illness Research Education and Clinical Center for Post Deployment Mental Health, Durham VA Medical Center, Durham, NC. 18) Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA. 19) Center for Behavioral Neuroscience, Yerkes National Primate Research Center, Atlanta, GA. 20) Howard Hughes Medical Institute, Bethesda, MD 21) Center of Human Genetics Research, Massachusetts General Hospital, Boston, MA. 22) Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA. 23) Department of Psychiatry, Harvard Medical School, Boston, MA. 24) Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL. 25) Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL. Short Title: The PGC-PTSD workgroup review. Abstract: 232 Body text: 5,233 References: 85 Tables: 2 Figures: 2 Supplementary files: 1 *Corresponding Author: Mark W. Logue Ph.D. Statistician, Research, VA Boston Healthcare System Research Assistant Professor, Biomedical Genetics, Boston University School of Medicine. Mail Stop 151C VA Boston Healthcare System 150 South Huntington Ave Boston MA 02130 Phone: 857-364-5665 Fax: 857-364-4501 [email protected]


Logue et al. 2

Abstract

The development of posttraumatic stress disorder (PTSD) is influenced by

genetic factors. Although there have been some replicated candidates, the

identification of risk variants for PTSD has lagged behind genetic research of other

psychiatric disorders such as schizophrenia, autism and bipolar disorder.

Psychiatric genetics has moved beyond examination of specific candidate genes in

favor of the genome-wide association study (GWAS) strategy of very large numbers

of samples, which allows for the discovery of previously unsuspected genes and

molecular pathways. The successes of genetic studies of schizophrenia and bipolar

disorder have been aided by the formation of a large-scale GWAS consortium: the

Psychiatric Genomics Consortium (PGC). In contrast, only a handful of GWAS of

PTSD have appeared in the literature to date. Here we describe the formation of a

group dedicated to large-scale study of PTSD genetics: the PGC-PTSD. The PGC-PTSD

faces challenges related to the contingency on trauma exposure and the large degree

of ancestral genetic diversity within and across participating studies. Using the PGC

analysis pipeline supplemented by analyses tailored to address these challenges, we

anticipate that our first large-scale GWAS of PTSD will comprise over 10,000 cases

and 30,000 trauma-exposed controls. Following in the footsteps of our PGC

forerunners, this collaboration—of a scope that is unprecedented in the field of

traumatic stress—will lead the search for replicable genetic associations and new

insights into the biological underpinnings of PTSD.


Logue et al. 3

Introduction

Posttraumatic stress disorder (PTSD) occurs in only a minority of persons

exposed to traumatic events (Breslau et al, 1998; Kessler et al, 1995). Factors that

influence PTSD susceptibility include sex, age, early life adversity, the nature and

timing of traumatic event exposure(s), the cumulative burden of these exposures, as

well as various other psychosocial and personality factors (Zoladz and Diamond,

2013). In the U.S., race/ethnicity impacts the rate, type, and age at traumatic-event

exposure, as well as the risk for development of PTSD after exposure (Roberts et al,

2011). Moreover, some events are more pathogenic than others. Events of an

interpersonal nature, e.g., rape, partner violence, and assault, confer greater risk of

developing PTSD than other types of trauma, e.g., natural disasters (Kessler et al,

1995). Twin studies have indicated that risk of exposure to some types of trauma

may itself be heritable, which is known as a gene-environment correlation (rGE)

effect. Lyons et al. (1993) using data from the Vietnam Era Twin Registry (Eisen et al,

1987; Goldberg et al, 1987), found that the heritability of combat exposure ranged

from 35 to 47%. In civilians, Stein et al. (2002) found that exposure to interpersonal

traumatic events was modestly heritable (~20%). The rGE for trauma exposure

appears to be largely explained by genetic influences on personality (Afifi et al,

2010; Jang et al, 2003). For example, sensation seeking is a heritable personality

trait that is characterized by engaging in behavior, such as driving at high speeds

(Zuckerman, 1994), which may increase the likelihood of trauma exposure. In

addition, the risk of PTSD following trauma exposure is moderately heritable, even


Logue et al. 4

after controlling for the genetic influences on trauma exposure. Twin studies

established that genetic influences explain a substantial proportion of vulnerability

to PTSD, from approximately 30% in male Vietnam veterans (True et al, 1993), to

38% in a sample of male and female civilians (Stein et al, 2002), with an upward

heritability estimate of 72% in young women (Sartor et al, 2011). This is

comparable to other internalizing disorders such as major depressive disorder and

panic disorder (Kendler and Prescott, 2007). Furthermore, genetic influences on

PTSD may overlap with those for other mental disorders. The genetic influences on

major depressive disorder and PTSD may substantially overlap (Fu et al, 2007;

Koenen et al, 2007; Sartor et al, 2012). Phenotypes like alcohol and drug

dependence (Sartor et al, 2011; Xian et al, 2000) and nicotine dependence (Koenen

et al, 2005) share approximately 40% genetic risk with PTSD. Genetic influences

common to generalized anxiety disorder and panic disorder symptoms account for

approximately 60% of the genetic variance in PTSD (Chantarujikapong et al, 2001).

The search for genetic markers of PTSD is a relatively new endeavor, with the

majority of studies conducted within the last decade. These investigations involve

genotyping (measuring variation at) a particular location along the genome.

Individuals’ particular genetic code, (genotype) at a location (locus) is then

compared for a sample of cases and controls. Most research to date has employed

the candidate-gene approach, in which genes are selected for study based on their

theorized involvement in biological pathways implicated in the pathophysiology of

PTSD. Given that PTSD has historically been conceptualized as a disorder of

pathological fear and stress (Wilker and Kolassa, 2013), most studies have focused


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on candidate genes involved in biological systems associated with the fear response,

including the hypothalamic-pituitary-adrenal (HPA) axis (e.g., FKBP5, CRHR1) and

the locus coeruleus-noradrenergic system (e.g., COMT, ADRB1 & ADRB2).

Additional work has examined serotonergic and dopaminergic systems involved in

the neural pathways underlying emotion (e.g., SLC6A4, SLC6A3), and systems

involved in memory consolidation and stabilization (e.g., WWC1, PRKCA). Candidate

gene studies of PTSD have produced a large body of literature (Pitman et al, 2012;

Wilker et al, 2013). However, candidate gene studies have, for the most part, failed

to replicate when the definition of replication is restricted to the observation of a

significant association in the same allele with the same effect direction (see Sullivan,

2007 for a discussion of replication in candidate genes studies). To date, relatively

few candidate gene studies of PTSD have examined gene-environment (GxE)

interactions, an approach that may be particularly well-suited for examining genetic

risk in PTSD. However, candidate gene GxE studies in psychiatric literature have

been prone to false positives and suitable replication has been difficult to obtain

(Duncan and Keller, 2011). Thus, as in the larger psychiatric genetics literature

(Psychiatric Gwas Consortium Coordinating Committee et al, 2009), for the most

part, robust support for markers associated with risk or resilience for PTSD has not

emerged from candidate gene studies.

In contrast to candidate gene studies, in a genome-wide association study

(GWAS), genetic variation—primarily single nucleotide polymorphisms (SNPs)—is

examined without hypothesizing the role of any particular gene or biological

function (Psychiatric Gwas Consortium Coordinating Committee et al, 2009). The


Logue et al. 6

viability of a GWAS strategy is predicated on the relatively low cost of chip-based

genotyping which reliably and cheaply assesses thousands or even millions of SNPs

distributed throughout the genome. Chip-based genotyping cannot yield

information about rare or even private (present in only one person or shared within

a particular family) mutations, except in the case of rare or private large copy

number variants (CNVs) which can be detected by examining the assays across

multiple SNPs. To examine other types of rare variants, more costly whole-genome

or whole-exome sequencing is required. Consequently, the investigation of rare

variants has primarily been addressed through sequencing, while common-variant

associations have been assessed through chip-based genotyping. It is customary to

examine hundreds of thousands or millions of SNPs in a single GWAS. As the number

of SNPs examined is great, and the number with individually detectable effects is

presumably small, strict multiple-testing control is required to reduce the number

of false positives. The current customary significance threshold is p<5x10-8 for a

genome-wide study regardless of the particular number of SNPs examined. This

strict threshold is useful in that it gives some assurance that the detected loci will be

robustly associated with the disorder under study.

To date, four GWAS of PTSD have been published (Guffanti et al, 2013; Logue

et al, 2013; Nievergelt et al, 2015; Xie et al, 2013). The genome-wide significant

findings of each are summarized in Table 1. Although the roles of these GWAS-

identified genes in risk for PTSD have not been elucidated, the top loci identified in

the extant GWAS have been implicated in a variety of processes, including

neuroprotection, actin polymerization, neuronal function, and immune function


Logue et al. 7

(Almli et al, 2014b; Guffanti et al, 2013; Logue et al, 2013; Xie et al, 2013). Notably,

the GWAS have identified variants in novel pathways that would not have been

examined using the biologically driven candidate-gene methodology. So far, the

findings from the different studies have not consistently implicated a primary set of

PTSD risk loci. Numerous factors may contribute to this including one or more of

them being false positives, heterogeneity across samples, and a statistical artifact of

the “winner’s curse” which implies that effect size estimates will be inflated for

moderately powered studies (Xiao and Boehnke, 2009). It is important to note that

samples sizes under 5,000 or even 10,000 are now considered to be relatively

'small' by modern GWAS standards (Sullivan et al, 2012). Convincing

demonstrations of association now come from GWAS of tens or even hundreds of

thousands of individuals (Lango Allen et al, 2010).

The PGC and Progress in Psychiatric Genetics.

While the results of the PTSD GWAS published to date may prove useful,

experience from GWAS of other psychiatric disorders has made it clear that large-

scale collaborations are necessary to yield consistently replicable findings. The

Psychiatric Genomic Consortium (PGC) was organized in 2007 as an outgrowth of

the Genetic Association Information Network (GAIN)—a joint public-private funded

venture to study attention deficit/hyperactivity disorder (ADHD), diabetic

nephropathy, major depressive disorder, psoriasis, schizophrenia and bipolar

disorder (Gain Collaborative Research Group et al, 2007). The PGC had as its goal to


https://www.researchgate.net/publication/237002707_Genome-wide_Association_Study_Identifies_New_Susceptibility_Loci_for_Posttraumatic_Stress_Disorder?el=1_x_8&enrichId=rgreq-b335a757-7b51-477b-883e-21206261019e&enrichSource=Y292ZXJQYWdlOzI3NTM1OTE0OTtBUzoyMjMyNDA4MjA2NjIyNzNAMTQzMDIzNjE2Nzc1Mw==

Logue et al. 8

conduct GWAS studies of ADHD, bipolar disorder, major depressive disorder, and

schizophrenia, and later autism spectrum disorder (Psychiatric Gwas Consortium

Coordinating Committee et al, 2009; The Psychiatric GWAS Consortium Steering

Committee, 2009). The PGC was designed to bring together psychiatric GWAS from

around the world to enable adequately powered analyses. By centralizing analyses

under a consortium umbrella, the PGC has overcome the substantial challenges of

harmonizing quality control procedures, analytic methods, and phenotype

definitions to enable GWAS meta- and mega-analyses (Sullivan, 2010). By

adequately powering analyses, and standing by strict definitions of significance

from the outset, the PGC has encouraged the production of high-quality replicable

genetic associations.

The PGC has become the largest collaborative effort in the history of

psychiatry and, as of this writing, comprises more than 500 scientists from more

than 100 countries. More than 172,000 subjects have been included, and genotyping

of an additional 90,000 is currently underway. PGC efforts have established that

sufficiently powered GWAS is a viable strategy for identifying neuropsychiatric

disorder susceptibility loci for bipolar disorder (Psychiatric GWAS Consortium

Bipolar Disorder Working Group, 2011) and schizophrenia (Ripke et al, 2011). The

PGC has enabled discovery of a large number of reliably associated genetic loci, 108

for schizophrenia alone at last count (Schizophrenia Working Group of the

Psychiatric Genomics, 2014). The PGC analyses have also given us an insight into the

genetic architecture of psychiatric disorders (Collins and Sullivan, 2013). In

particular, these analyses have demonstrated that psychiatric disorders are


Logue et al. 9

polygenic (having hundreds or even thousands of risk loci) and that common

variation accounts for a substantial component of the underlying genetic

architecture. Their results have indicated that GWAS-significant loci represent the

tip of the iceberg in terms of the proportion of variance explained by inherited

genetic variation, and the remaining variation is likely to represent a mix of rare and

common genetic effects. For example, in schizophrenia, the proportion of variation

explained by the 108 genome-wide significant loci was 3.4% (Schizophrenia

Working Group of the Psychiatric Genomics, 2014), while estimates of the total

proportion of variation explained by common genotyped SNPs has been estimated

to be approximately 25% and 45% depending on the population and method used

(International Schizophrenia et al, 2009; Lee et al, 2012; Ripke et al, 2013). In

addition to common variants, rare variants such as CNVs were found to explain a

proportion of risk for schizophrenia, bipolar disorder, and autism (Malhotra and

Sebat, 2012). Results in schizophrenia also suggest that many of the genome-wide

significant loci obtained at smaller sample sizes will turn out to be significant once

the sample size gets large (Schizophrenia Working Group of the Psychiatric

Genomics, 2014). The polygenic nature of the psychiatric disorders is such that once

the sample size is sufficiently large, the genome-wide distribution of association

statistics will differ from the expected null distribution (Schizophrenia Working

Group of the Psychiatric Genomics, 2014). A new method called LD regression has

been developed to test whether or not genomic inflation in this context represents a

polygenic risk component to disease or inflated significance due to population

substructure (Bulik-Sullivan et al, 2015b).


Logue et al. 10

Also importantly, as the list of risk loci has expanded, they have begun to

coalesce into biological pathways, illuminating disease pathogenesis and implicating

new targets for drug development (Cross-Disorder Group of the Psychiatric

Genomics Consortium, 2013; Nurnberger et al, 2014; Schizophrenia Working Group

of the Psychiatric Genomics Consortium, 2014). For example, recent analyses have

highlighted the role of immune system and glutamatergic function in schizophrenia

(Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014) and

calcium channel signaling across childhood- and adult-onset disorders (Cross-

Disorder Group of the Psychiatric Genomics Consortium, 2013). The PGC Cross-

Disorder Workgroup identified several loci that appear to confer risk across autism,

ADHD, bipolar disorder, major depressive disorder, and schizophrenia (Cross-

Disorder Group of the Psychiatric Genomics Consortium, 2013). Aggregate genome-

wide analyses (using SNP-heritability estimates and polygene scores) showed

significant genetic overlap among these disorders, with the strongest overlap

between bipolar disorder and schizophrenia (genetic correlation = +0.68; Cross-

Disorder Group of the Psychiatric Genomics Consortium et al, 2013). By highlighting

shared biologic vulnerability, this work may inform efforts to refine psychiatric

nosology. Recently Bulik-Sullivan et al. (2015a) have developed a new

computationally efficient technique for estimating cross-trait genetic correlation

based on LD regression. The results obtained with this new method mirror earlier

work showing genetic correlation between schizophrenia and bipolar disorder.

However, this new method has the advantage that it can be run on summary

statistics from both traits, rather than necessitating individual-level data.


Logue et al. 11

The PGC has also led the development of the PsychChip. The PsychChip is an

Illumina (San Diego, CA, USA) genotyping chip that assesses approximately 560,000

markers. It is designed to be suitable for analysis of psychiatric traits and for use in

the imputation of genome-wide SNP genotypes (described in the Supplementary

Materials).

The PGC-PTSD Workgroup

Drs. Koenen, Ressler and Liberzon founded the PCG-PTSD Workgroup (PGC-

PTSD) in May 2013 with a satellite meeting at the Society of Biological Psychiatry

co-sponsored by NIMH and One Mind, a patient advocacy non-profit organization

(http://onemind.org). The PGC-PTSD has as its goal the bringing together of a large

number of PTSD researchers for the purpose of large-scale GWAS studies of PTSD.

The Sample: The size and characteristics of the groups anticipated to

participate in the PGC-PTSD are summarized in Table 1. First, six groups have

uploaded genotype data that will be used in the initial PGC-PTSD analysis. This

includes a combined sample size of 20,468 subjects (4,487 cases and 15,981

controls). Second, an additional 53,552 subjects from 13 studies have been

genotyped (19,408 cases and 34,144 controls). Third, there are 20 studies with

genotyping in process or planned (N=71,757; 24,439 cases and 47,318 controls).

Many of these studies will be using the PsychChip. Data collection sites are from

across the U.S. (e.g., Atlanta, San Diego, New Haven, Detroit) and include three

additional countries (Denmark, the Netherlands and South Africa). Like other PGC


Logue et al. 12

disorders, we expect that this initial sample is merely the first foray into large-scale

meta-analyses.

The vast majority (>80%) of controls across these studies have experienced a

trauma fulfilling the exposure criterion for PTSD. Hence, the PGC-PTSD sample will

have a large trauma-exposed control group available for comparison to PTSD cases.

Focusing on trauma-exposed individuals may be useful, as any PTSD risk allele,

which will have an increased rate in PTSD cases, will presumably have a lower

frequency in PTSD-negative trauma-exposed controls than in trauma negative or

unscreened controls. Hence, all other things being equal, the greater difference in

allele frequency between PTSD cases and trauma-exposed controls will lead to

greater power to detect associations than a sample that includes trauma-negative or

unscreened controls. Utilizing unscreened controls in the presence of rGE effects

could result in associations representing a mix of loci, some of which were

associated with risk of PTSD in the presence of trauma exposure and some of which

were related to the risk of trauma exposure itself. The use of only trauma-exposed

controls and inclusion of degree of trauma exposure as a covariate in analyses

should be adequate to place our focus tightly on loci that increase risk of PTSD

directly.

Phenotype and exposure measurement complexity: The harmonization

of data across PGC-PTSD groups, like that for other psychiatric disorders, is

complicated by variability in the assessment methods used. Two major approaches

to the assessment of PTSD symptoms and diagnosis—structured clinical interviews

and self-report instruments—are represented, with the primary distinction


Logue et al. 13

between them related to the source of the data (i.e., clinician ratings versus

participant self-report). Of the 6 samples already uploaded to the PGC-PTSD, 5 used

self-report measures and one used clinician ratings. The major limitation of clinical

interviews is the considerable time and expense involved in training and

administration, which renders this approach impractical for many studies. Studies

featuring some of the largest samples have used self-report instruments to assess

symptoms and estimate diagnosis. Additionally, although not yet investigated within

the PGC-PTSD group, there is the possibility of using diagnostic information from

additional sources such as from electronic health records, which can provide

evidence of convergent validity. Finally, methods for determining diagnostic status

(i.e., cases versus controls) differ between interview and self-report approaches, as

well as across traumatized populations. Interview-based studies, based on the

judgment of trained clinicians, typically apply the DSM algorithm (i.e., for DSM-IV,

one reexperiencing symptom, three avoidance and numbing symptoms, and two

hyperarousal symptoms). With self-report measures, diagnostic classifications are

somewhat less straightforward. The DSM-IV algorithm can also be applied by

defining symptoms endorsed above a given severity threshold level as present (i.e.,

causing moderate or greater distress). However, patterns of item endorsement tend

to vary across items and populations, so the application of a uniform criterion can

yield significant differences in composition of cases across samples. Alternatively,

PTSD diagnostic status can be determined in relation to a total symptom severity

score cutoff. Studies that have examined the relationship between probable

diagnoses derived from this approach versus interview measures of PTSD have


Logue et al. 14

found acceptable, though not excellent, agreement (see e.g. McDonald et al, 2009;

McDonald and Calhoun, 2010). Studies have shown that for any given measure the

optimal score for differentiating cases from controls differs across samples and can

be influenced by a host of factors, most notably, the base rate of the diagnosis in the

sample (for a meta-analysis of PCL studies, see Terhakopian et al, 2008). Thus,

because the same instrument can yield different classification performance across

different samples, our cutoff score selections will take into account independent

estimates of the true base rate of the sample.

The harmonization of measures of trauma exposure across studies is an

additional complication for PTSD genetics research. Though the DSM offers a broad

definition of the types of events known to cause PTSD, there is no uniform or

generally agreed-upon framework for categorizing or measuring them. A variety of

self-report measures of trauma exposure are represented among PGC-PTSD studies.

Most consist of a list of events that meet the DSM-IV PTSD Criterion A1 trauma

definition including exposure to sexual or physical assault, combat or warfare,

sudden death of friend/loved one, etc. Most also make it possible to re-classify

events for harmonization purposes into broader categories such as childhood

versus adult trauma, or interpersonal versus non-interpersonal trauma, or to

compute a measure of total trauma load (i.e., a sum of event exposure categories

across the lifespan).

The instruments used in the various studies also differ with respect to the

way that they link PTSD to the trauma. In clinical interview instruments such as the

Clinician Administered PTSD Scale for DSM-IV (CAPS-IV; Blake et al, 1990) and the


Logue et al. 15

Structured Clinical Interview for DSM-IV Axis I Disorders (SCID; First et al, 1994),

interviewers identify an index event(s) and then evaluate its link to subsequent

symptoms while accounting for confounding factors such as comorbidity, substance

abuse, medical issues, and reporting style. For self-report measures (e.g. the PTSD

Checklist [PCL]; Weathers et al, 1993), approaches range from those that link

symptoms to a single event, to those that do not reference a single event, to those

that reference military experience broadly.

Ancestry: Most extant PGC GWAS have been restricted to a single ancestral

population because population stratification can lead to Type I and Type II errors in

genetic association studies (Marchini et al, 2004). Psychiatric research in the U.S.

and Europe has traditionally enrolled a relatively large proportion of subjects of

European ancestry, and consequently, GWAS in the PGC have been performed

primarily using subjects of European ancestry (Figure 1A). In contrast, PTSD studies

have recruited subjects primarily from high-risk populations, such as combat-

veteran cohorts, or in urban areas with high rates of violent crime, and thus PGC-

PTSD samples include a large proportion of subjects of African American and

Hispanic/Latino ancestry (Figure 1B). GWAS on such heterogeneous and admixed

samples require additional considerations (e.g., Pasaniuc et al, 2011). Combining

across multiple ancestry groups via meta-analysis has become more common in the

recent past (see e.g. Nievergelt et al. 2014 and Li and Keating, 2014 for review).

.

Research Strategy


Logue et al. 16

PTSD meta-analysis: Our proposed analysis strategy is described in the

Supplementary Materials and is briefly outlined here. Standardized quality control

procedures and GWAS analysis based on the PGC GWAS-analysis pipeline will be

used (Ripke et al, 2013). Harmonized versions of continuous predictive variables

and outcomes (e.g. PTSD severity) will be generated based on within-study

normalization of the instruments available. Categorical outcome and predictor

variables will, for the most part, be based on the diagnostic schema adopted by the

principal investigator of the particular study taking into account sample- and

measurement- factors that affect prevalence estimates. The efficacy of the

harmonization will be assessed using descriptive statistics and by examining

correlations between predictive variables, outcomes, and reported demographic

information from each group. Our primary analysis will be a GWAS meta-analysis of

PTSD followed by a GWAS of PTSD severity, both controlling for potential sources of

bias as well as trauma-exposure variables. Based on a consensus of participating

group members at in-person PGC-PTSD planning meetings, we determined to utilize

dichotomous DSM-IV diagnosis as the primary phenotype. Initially, this analysis will

be restricted to trauma-exposed controls. The pipeline will be modified to account

for greater population stratification between and within PGC-PTSD groups

compared to the typical PGC analysis. Both within-ancestral group and cross-

ancestral group meta-analysis will be performed. Subsequent investigation will

include analyses of rare variants, including structural variants such as copy number

variation (CNV).


Logue et al. 17

The PGC-PTSD has already assembled a substantial aggregate sample size, as

well as an extensive set of samples that will be genotyped if funding allows. The

power to detect a SNP effect in a GWAS analysis varies as a function of the size of the

effect, the allele frequency of the SNP, and the sample size. This is illustrated in

Figure 2, which displays the minimum effect size that yields 80% power as a

function of the SNP allele frequency and sample size. An analysis including the

45,000 the samples that have currently funded genotyping will have 80% power to

detect a locus with a genotype relative risk (GRR) between 1.2 and 1.11 for allele

frequencies between 5% and 20%. Increasing the sample size to 60,000 will allow

us to detect a locus with a GRR between 1.17 and 1.1, respectively.

GxE analyses. In addition to the standard GWAS meta-analysis, a secondary

aim of the PGC-PTSD is to conduct a series of GxE analyses. Some readers may be

surprised that this is not the primary analysis for PTSD. Although we are well aware

of the conceptual relevance of GxE models to PTSD, the statistical challenges

associated with GxE analyses are formidable. First, although PTSD clearly results

from the interaction of trauma with genetic predisposition, it is unclear whether or

not the biological realities of such an interaction are captured by testing deviations

from a multiplicative logistic regression model (Thompson, 1991). Second, the

significance of the GxE interaction term estimated using standard regression models

can be inflated under commonly occurring conditions (Almli et al, 2014a; Voorman

et al, 2011). Third, obtaining reasonable power in GxE analysis takes sample sizes

larger than those required for main effect analyses. A sample four times as large has

been proposed as a rule of thumb (Thomas, 2010). Finally, the power and bias of


Logue et al. 18

different GxE analysis methods vary depending on the nature of the interaction

(Cornelis et al, 2012; Mukherjee et al, 2012).

Approaches used previously in PTSD-genetics studies have ranged from

including cumulative lifetime ‘trauma load’ as a covariate in the analysis (Kolassa et

al, 2010) to explicitly testing for GxE interactions (Digangi et al, 2013). To date,

there have been no genome-wide GxE studies of PTSD. Although the single genome-

wide GxE study published in psychiatry to date (a study of ADHD) did not yield

significant findings (Sonuga-Barke et al, 2008), genome-wide GxE studies have been

successful in other areas (e.g. Beaty et al, 2011).

The PGC-PTSD will use a two-stage strategy to examine GxE effects. First,

given the likelihood of developing PTSD increases with exposure to childhood

trauma, interpersonal violence, and with increasing trauma load, GxE models will

test the hypothesis that the effects of risk variants for PTSD (identified through the

primary GWAS) are moderated by these environmental variables. The second

approach is a 'genome-wide GxE’ meta-analysis approach that will systematically

interrogate the genome for GxE effects between SNPs and these three

environmental variables. This will include fitting a logistic regression model of PTSD

and linear model of PTSD severity as a function of a SNP x childhood trauma, SNP x

interpersonal trauma, and SNP x total trauma load interaction effects using robust

standard errors to combat genome-wide inflation of significance. Follow-up

analyses will examine the effect of multiple characteristics of trauma exposure,

including trauma load, type, timing, and severity. Finally, we note that the data


Logue et al. 19

gathered here will provide a resource for secondary analysis and methodological

development, as has been the case for other PGC disorders such as schizophrenia.

Comorbidity. PTSD is highly comorbid with other psychiatric disorders, and

a substantial proportion of this comorbidity may be explained by common genetic

influences (Koenen et al, 2003; Wolf et al, 2010). Hence, in this context comorbidity

may present an opportunity to explore potential overlapping genetic effects in our

sample. We propose to follow the PGC cross-disorder model and perform a

polygenic architecture analysis with polygenic risk scores and LD regression to

determine the proportion of genetic variance (heritability) common across PTSD

and other psychiatric disorders.

PGC-PTSD Subgroups.

The PGC-PTSD also represents the confluence of vast reserves of PTSD-

related information that will enable large-sample investigations of PTSD-associated

epigenetic, neuroimaging, and other neurobiological measures. In order to facilitate

the analysis of these data, a pair of focus groups have been created within the PGC-

PTSD workgroup: the PGC-PTSD Epigenetics Workgroup and the PGC-PTSD

Neuroimaging Genetics Workgroup.

PGC-PTSD Epigenetics Workgroup. Recent, “stand alone” genome-scale

studies of PTSD epigenetics and gene expression have provided initial insight into

molecular dysregulation associated with the disorder (Mehta et al, 2013).

Epigenetics provides a molecular context to GxE interactions by offering a biological

mechanism through which gene expression can vary in response to an


Logue et al. 20

environmental exposure (see e.g. Latham et al, 2012). Genetic variation has been

shown to influence DNA methylation and gene expression levels, often in tissue-

specific and developmental stage-specific manners; so-called methylation trait

quantitative loci (meQTLs) and expression trait quantitative loci (eQTLs) have been

identified across the genome in numerous studies to date (see e.g. Smith et al, 2014).

Although genome-scale studies of PTSD-associated meQTLs and eQTLs have yet to

be reported, focused candidate gene studies have revealed notable examples of each

(see e.g. Klengel et al, 2013; Mehta et al, 2011; Ressler et al, 2011). Within the PGC-

PTSD, there are several groups with both genome-wide genotype and methylation

data, with a current total n=1,114. The PGC-PTSD Methylation Workgroup has as its

goal the creation of a large PTSD-focused methylation dataset that can be used to

identify whether gene expression or methylation act as mediators of the association

between SNPs and PTSD risk as well as identifying PTSD-relevant eQTLs and

meQTLs that can be examined for association to PTSD and trauma exposure.

PGC-PTSD Neuroimaging Workgroup. The PGC group members have a

large number of participating groups with neuroimaging data. Within the PGC-PTSD

there are over 5,000 samples that will have both structural MRI and GWAS data

available. Smaller datasets of DTI, resting-state fMRI, MEG and other imaging

modalities are also available. These data will allow the investigation of how genomic

markers modulate neuroimaging quantitative traits (QTs) associated with PTSD.

The uncertainty associated with psychiatric nosology makes reference to an

intermediate biological variable attractive, as the heritability of intermediate

phenotypes such as regional brain volumes is often 80% or higher (den Braber et al,


Logue et al. 21

2013). However, these will not represent a magic bullet. Given the results of the

ENIGMA group, a neuroimaging GWAS meta-analysis consortium, effect sizes

observed for individual SNPs on brain structures are likely to be modest and require

large sample sizes to be adequately powered (Hibar et al, 2015; Stein et al, 2012).

The PGC-PTSD Neuroimaging Workgroup will facilitate the creation of a large PTSD-

focused neuroimaging dataset to investigate genomic markers for association to

cortical and subcortical volumes such as hippocampus, amygdala, and medial

prefrontal cortex structures as well as regional cortical thickness changes that are

associated with PTSD. Genomic markers found to predict imaging QTs may play a

role in PTSD symptoms or diagnoses (Meyer-Lindenberg and Weinberger, 2006).

Discussion

There are several ways in which the PGC-PTSD will represent and advance

the current cutting-edge of PTSD genetics research. First and foremost, the PGC-

PTSD will build on what the PGC has learned in other disease domains. We believe

that the PGC-PTSD, through its investigation of genetic variation, epigenetic

variation, and neuroimaging characteristics of PTSD will provide new and important

insights into the biological underpinnings of PTSD risk. The PGC-PTSD additionally

has the goal of developing clinically useful biomarkers of PTSD. The work of the

PGC-PTSD will inform the development of at least three types of clinical biomarkers.

The first are predictive biomarkers that reliably distinguish persons at high versus

low risk for the development of PTSD following trauma. A gene or combination of

genes associated with PTSD may, in conjunction with other information, contribute


Logue et al. 22

to an algorithm for estimating the risk of developing PTSD. Such a risk algorithm

could be used in first-response settings or the military to better target preventive

interventions.

The second type of biomarker likely to be informed by the discoveries of the

PGC PTSD working group is related to treatment matching. There are several

effective interventions for PTSD including prolonged exposure, cognitive processing

therapy, skills training in affective and interpersonal regulation, and

pharmacological interventions. However, little is known about which of these

treatments might be most effective for which patients. One of the long-term goals of

the PGC-PTSD will be to examine whether patients with specific combinations of

genetic variants and environmental exposures respond differentially to evidence

based treatments.

The third type of biomarker that may be informed by the work of the PGC-

PTSD is relapse prediction. Several of the studies included in the PGC-PTSD meta-

analysis are longitudinal and a few are truly prospective (Baker et al, 2012;

Goldmann et al, 2011). Thus, we will be able to examine whether genetic variants

associated with PTSD also predict the clinical course of the disorder. If patients with

a specific combination of genetic and environmental risk factors are at higher risk of

relapse, such patients could be targeted with relapse prevention strategies.

Knowledge of the genetic and environmental architecture of PTSD has the

potential to advance our understanding of the pathophysiology of the disorder and

inform treatment development. Of particular interest is the development of

preventive pharmacological interventions for PTSD that could be administered in


Logue et al. 23

the acute aftermath of traumatic events. Many pharmacological agents have been

explored in this regard including propranolol and hydrocortisone, but none have

shown decisive efficacy for PTSD prevention in large RCTs. The success of GWAS of

schizophrenia and bipolar disorder has led to the identification of new treatment

targets (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013;

Nurnberger et al, 2014; Schizophrenia Working Group of the Psychiatric Genomics

Consortium, 2014). Clinical trials are underway to determine whether these will

translate into more effective treatments. Rather than simply generating a list of

associated DNA variants, our goal is to produce the same successes for PTSD.

Funding and Disclosures: Support for this study has been provided by One Mind. Dr. Morey was supported by Department of Veterans Affairs (DVA) 1I01CX000748-01A1 and I01CX000120-01 and the Mid-Atlantic Mental Illness, Research, and Education Center. Murry B. Stein:Consultant: Care Management Technologies; Janssen, Pfizer, and Tonix Pharmaceuticals Editorial Work: Up-To-Date; Biological Psychiatry (journal); Depression and Anxiety (journal). Dr. Ressler currently receives funding from NIH (NIMH) and HHMI. In the past, he has received funding from the Burroughs Wellcome Fund, and the Brain and Behavior Fund. He is a founding member of Extinction Pharmaceuticals/Therapade Technologies to develop D-cycloserine, a generically available compound, for use to augment the effectiveness of psychotherapy. He has received no equity or income from this relationship within the last 5 years. Jordan W. Smoller has received funding from ProMedica Klarman Family Foundation ANGI Welcome Trust Strategic Awards Committee (London). In the past 3 years, MWL has received funding from the US. Department of Veterans Affairs, the NIH, and the Thome Memorial Foundation. Dr. Smith receives or has received


Logue et al. 24

research support from the American Foundation for Suicide Prevention, Schering Plough Pharmaceuticals, NARSAD (YI 19233), the Conquer Cancer Foundation and NIH (MH085806, MH088609, DK100392 and MD009064). The other authors of this study have no conflicts of interest to report.

Supplementary information is available at the Neuropsychopharmacology website


Logue et al. 25

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Logue et al. 33

Tables: Table 1. Results of GWAS of PTSD.

Discovery Sample(s)

Replication Sample(s)

First Author (year) # Markers # Cases

# Control

s Sample Gender Ancestry # Cases #

Controls Characteristics Locus Genes (SNP) p-value Logue et al. (2012)

2.5 million

295 196 Trauma-exposed veterans and their intimate partners

60% male

EA a) 43 41 AA trauma-exposed veterans and their intimate partners

15q22.2 RORA (rs8042149)

2.5 x 10-8

b) 100 421 AA trauma-exposed community sample

Xie et al. (2013)

~870,000 a) 300 1,278 Participants in genetics of substance use studies; trauma-exposed controls only in secondary analysis

60% male

EA a) 207 1,692 EA participants in genetics of substance use studies

7p12

4q32

COBL (rs406001)

TLL1

(rs6812849)

3.97 x 10-8

2.99 x 10-7

b) 444 2,322 Participants in genetics of substance use studies; trauma-exposed controls only in secondary analysis

55% male

AA b) 89 655 AA participants in genetics of substance use studies

--- --- ---

Guffanti et al. (2013)

730,525 94 319 Trauma-exposed community sample

100% female

83% AA 578 1,963 EA trauma-exposed female nurses

2q32.1 Long intergenic

non-coding RNA

AC068718.1 (rs10170218)

5.09 x 10-8

Nievergelt et al. (2015)

888,113 directly genotyped; >21 million imputed

940 2,554 Trauma-exposed military sample

100% male

86% EA, 25%

Hispanic1

313 178 EA trauma-exposed veterans and their intimate partners

10p12.1 PRTFDC1 (rs6482463)

2.04 x 10-9

Note. GWAS=genome-wide association study. EA=European American ancestry. AA=African American ancestry. 1Conducted analyses separate


Logue et al.

34

Table 2. Summary of Participating PGC-PTSD groups.

Prev

ious

ly G

enot

yped

Sam

ples

Principal Investigator Sample Name Cases Controls Total Ancestry Illumina

Platform Ressler, Kerry Grady Trauma Project 1,503 3,249 4,752 AA - Mixed 1M Omni-Quad

Aiello, Allison Detroit Neighborhood Health Study 192 620 812 AA OmniExpress

Gelernter, Joel Genetics of Substance Dependence 818 4,633 5,451 60% EA OmniQuad

Nievergelt, C. Marine Resilience Study 538 3,477 4,015 EA 60% OmniExpressExome

Bierut, Laura Family Study of Cocaine Dependent 471 3,568 4,039 Mixed 1M Beadchip

Miller, Mark Boston-VA 600 500 1,100 Mixed Omni 2.5M

Stein, Murray Army STARRS 4,500 15,500 20,000 Mixed OmniExpressExomeC

Beckham, Jean MIRECC 1,156 1,156 2,312 Mixed 650/1M-Duo/Omni2.5

Ressler, Kerry Grady Trauma Project 497 1,751 2,248 AA - Mixed 1M Omni-Quad

Stein, Murray VA Cooperative Study 10,000 10,000 20,000 Mixed OmniExpressExomeC

DeLisi, Lynn UCSD VA 1,000 1,000 2,000 Mixed Smith, Nicholas VET Study 492 377 869 Mixed Hollegaard, Mads Danish Blood Spot Cohort 500 2,500 3,000 EA Subtotal 22,267 48,331 70,650

Sam

ples

wit

h Fu

nded

Ge

noty

ping

Koenen, Karestan Nurses Health Study II 680 700 1,380 EA PsychChip Liberzon, Israel Ohio national Guard Study 170 200 370 EA PsychChip Lyons, Michael Vietnam Era Twin Registry 350 350 700 EA PsychChip Ressler, Kerry / Dan Stein

Civilian South African Cohort 200 400 600 S. African PsychChip

Vermetten, Eric Military Research (PRISMO) 35 965 1,000 EA PsychChip Bryant, Richard

Australian Injury Vulnerability Study 205 796 1,001 EA Pysch Chip

Ressler, Kerry Predictive Biomarkers Project 200 400 600 80% AA PsychChip

Subtotal 1,840 3,811 5,651

Addi

tion

al S

ampl

es id

enti

fied

for

futu

re g

enot

ypin

g on

ce fu

ndin

g is

ob

tain

ed.

Ressler, Kerry Grady Trauma Project* 200 1,000 1,200 AA - Mixed Ressler, Kerry / Holly Orcutt NIU Shooting Sample 70 230 300 80% EA

Aiello, Allison Detroit Neighborhood Health Study 72 197 269 AA

Liberzon, Israel

Gracy Detroit Mother's Study 200 220 420

75%EA, 23%AA

Liberzon, Israel Ohio national Guard Study 10 860 870

85%EA, 13%AA

Koenen, Karestan Nurses Health Study II 170 1,463 1,633 EA Kessler, Ronald

World Mental Health Surveys 318 6,969 7,287 Other

Amstadter, Ananda

Service Experience and Alcohol Preference Study 80 80 160

80%EA, 20%AA

Amstadter, Ananda

Disaster-Affected Adolescents and Families 82 698 780

70%EA, 25% AA

Yehuda, Rachel Improving PTSD Outcomes in OIF/OEF Returnees 121 300 421 Mixed

Yehuda, Rachel Suicidality and PTSD 90 0 90 Mixed Yehuda, Rachel Holocaust PTSD 45 0 45 EA

Feder, Adriana World Trade Center Responders 50 200 250 Mixed

Baker, Dewleen Marine Resiliency Study* 117 583 700 EA 60%


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Stein, Murray Army STARRS 1,800 12,200 14,000

Bradley, Bekh

Genetic and Environmental Risk/Resilience Factors for Posttraumatic Stress Disorder in OEF/OIF Veterans 200 600 800 AFR

Beckham, Jean MIRECC* 152 758 910 Mixed

Herringa, Ryan Neural Basis of Emotion Regulation in PTSD 50 50 100

Bisson, Jonathan Wales PTSD Study 462 960 1,422 EA Hollegaard, Mads Danish Blood Spot Cohort 20,000 20,000 40,000 EA

Risbrough, Victoria

Norman VA Exposure Therapy 200 0 200 60% EA

Subtotal 25,489 51,368 76,857 TOTAL 48,596 99,510 148,158


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Figures Legends: Figure 1: A comparison of ancestral diversity in A) representative Psychiatric Genomics Consortium (PGC) samples of primarily European ancestry and B) representative PGC-PTSD samples. Figure 1 Key: mrsa , mrsb - subsets (a,b) of the Marine Resilience Study (Nievergelt); gtpx - Grady Trauma Project (Ressler); gsdx - Genetics of Substance Dependence (Gelernter); fscd - Family Studies of Cocaine Dependence (Bierut); dnhs - Detroit Neighborhood Health Study (Aiello); cogb , coga - subsets (a,b) of the COGEND study (Bierut); Note that African American refers to subjects from the USA who typically have a mix of African and European ancestry, while African Ancestry refers to subjects from Africa without admixed ancestry. Figure 2: Effect size necessary to have 80% power for case-control and quantitative-trait association analyses demonstrating the relation between increasing sample size and ability to detect loci of smaller effect sizes. Figure 2 Key: Calculated assuming PTSD prevalence of 15%, additive model, a type I error rate of 5x10-8, and perfect LD between marker and trait allele for MAF>5%). Calculations were based on a 1:3 PTSD case-control ratio or quantitative traits such as PTSD symptoms.


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